Mutation bias interacts with composition bias to influence adaptive evolution
Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each...
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description | Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias-a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths-that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness. |
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Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias-a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths-that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1008296</identifier><identifier>PMID: 32986712</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Affinity ; Bias ; Binding ; Binding sites ; Bioinformatics ; Biology and life sciences ; Composition ; Computational Biology ; Deoxyribonucleic acid ; DNA ; DNA, Bacterial - chemistry ; DNA, Bacterial - genetics ; DNA, Bacterial - metabolism ; Evolution ; Evolution & development ; Evolution, Molecular ; Evolutionary adaptation ; Gene expression ; Gene sequencing ; Genetic aspects ; Genetic diversity ; Genotype ; Genotype & phenotype ; Genotypes ; Influence ; Models, Genetic ; Mutation ; Mutation - genetics ; Mutation - physiology ; Nucleotide sequence ; Phenotype ; Phenotypes ; Physical Sciences ; Physiological aspects ; Population ; Population genetics ; Protein Binding - genetics ; Proteins ; Software ; Stochastic processes ; Stochasticity ; Topography ; Transcription factors ; Transcription Factors - chemistry ; Transcription Factors - genetics ; Transcription Factors - metabolism</subject><ispartof>PLoS computational biology, 2020-09, Vol.16 (9), p.e1008296-e1008296</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Cano, Payne. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Cano, Payne 2020 Cano, Payne</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c633t-3a20427c0f09db2549d285d937f898d9ade8ac5f811ade97b844521bdc85092e3</citedby><cites>FETCH-LOGICAL-c633t-3a20427c0f09db2549d285d937f898d9ade8ac5f811ade97b844521bdc85092e3</cites><orcidid>0000-0003-4728-8489</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571706/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571706/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23853,27911,27912,53778,53780,79355,79356</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32986712$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Radivojac, Predrag</contributor><creatorcontrib>Cano, Alejandro V</creatorcontrib><creatorcontrib>Payne, Joshua L</creatorcontrib><title>Mutation bias interacts with composition bias to influence adaptive evolution</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias-a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths-that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.</description><subject>Adaptation</subject><subject>Affinity</subject><subject>Bias</subject><subject>Binding</subject><subject>Binding sites</subject><subject>Bioinformatics</subject><subject>Biology and life sciences</subject><subject>Composition</subject><subject>Computational Biology</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA, Bacterial - chemistry</subject><subject>DNA, Bacterial - genetics</subject><subject>DNA, Bacterial - metabolism</subject><subject>Evolution</subject><subject>Evolution & development</subject><subject>Evolution, Molecular</subject><subject>Evolutionary adaptation</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Genetic aspects</subject><subject>Genetic diversity</subject><subject>Genotype</subject><subject>Genotype & phenotype</subject><subject>Genotypes</subject><subject>Influence</subject><subject>Models, Genetic</subject><subject>Mutation</subject><subject>Mutation - genetics</subject><subject>Mutation - physiology</subject><subject>Nucleotide sequence</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Population</subject><subject>Population genetics</subject><subject>Protein Binding - genetics</subject><subject>Proteins</subject><subject>Software</subject><subject>Stochastic processes</subject><subject>Stochasticity</subject><subject>Topography</subject><subject>Transcription factors</subject><subject>Transcription Factors - chemistry</subject><subject>Transcription Factors - genetics</subject><subject>Transcription Factors - metabolism</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEomXhGyCIxAUOu_gZ2xekquKxUgsSj7Pl2JOtV0m8jZ0Fvj0Om5Yu4oJ88Gj8m_88PEXxFKMVpgK_3oZx6E272tnarzBCkqjqXnGKOadLQbm8f8c-KR7FuEUom6p6WJxQomQlMDktLi_HZJIPfVl7E0vfJxiMTbH87tNVaUO3C9H_eU8hI007Qm-hNM7skt9DCfvQjhP0uHjQmDbCk_leFN_evf16_mF58en9-vzsYmkrStOSGoIYERY1SLmacKYckdwpKhqppFPGgTSWNxLjbCpRS8Y4wbWzkiNFgC6K5wfdXRuinicRNWEcc1ap3OiiWB8IF8xW7wbfmeGnDsbr344wbLQZkrct6Dw5VgFyFZCGKUmVwIwAp1Jwgwyvs9abOdtYd-As9Gkw7ZHo8Uvvr_Qm7LXgAgtUZYGXs8AQrkeISXc-Wmhb00MYp7qZoEgyJDL64i_0392tDtTG5Abyj4Sc1-bjoPM29ND47D-rqESIVXKq4NVRQGYS_EgbM8ao118-_wf78ZhlB9YOIcYBmtupYKSnNb0pX09rquc1zWHP7k70NuhmL-kvL5Hjhw</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Cano, Alejandro V</creator><creator>Payne, Joshua L</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4728-8489</orcidid></search><sort><creationdate>20200901</creationdate><title>Mutation bias interacts with composition bias to influence adaptive evolution</title><author>Cano, Alejandro V ; Payne, Joshua L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c633t-3a20427c0f09db2549d285d937f898d9ade8ac5f811ade97b844521bdc85092e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptation</topic><topic>Affinity</topic><topic>Bias</topic><topic>Binding</topic><topic>Binding sites</topic><topic>Bioinformatics</topic><topic>Biology and life sciences</topic><topic>Composition</topic><topic>Computational Biology</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA, Bacterial - 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chemistry</topic><topic>Transcription Factors - genetics</topic><topic>Transcription Factors - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cano, Alejandro V</creatorcontrib><creatorcontrib>Payne, Joshua L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cano, Alejandro V</au><au>Payne, Joshua L</au><au>Radivojac, Predrag</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mutation bias interacts with composition bias to influence adaptive evolution</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>16</volume><issue>9</issue><spage>e1008296</spage><epage>e1008296</epage><pages>e1008296-e1008296</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias-a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths-that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32986712</pmid><doi>10.1371/journal.pcbi.1008296</doi><orcidid>https://orcid.org/0000-0003-4728-8489</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptation Affinity Bias Binding Binding sites Bioinformatics Biology and life sciences Composition Computational Biology Deoxyribonucleic acid DNA DNA, Bacterial - chemistry DNA, Bacterial - genetics DNA, Bacterial - metabolism Evolution Evolution & development Evolution, Molecular Evolutionary adaptation Gene expression Gene sequencing Genetic aspects Genetic diversity Genotype Genotype & phenotype Genotypes Influence Models, Genetic Mutation Mutation - genetics Mutation - physiology Nucleotide sequence Phenotype Phenotypes Physical Sciences Physiological aspects Population Population genetics Protein Binding - genetics Proteins Software Stochastic processes Stochasticity Topography Transcription factors Transcription Factors - chemistry Transcription Factors - genetics Transcription Factors - metabolism |
title | Mutation bias interacts with composition bias to influence adaptive evolution |
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